Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model
AbstractNeural networks are well suited to predict future results of time series for various data types. This paper proposes a hybrid neural network model to describe the results of the database of the New York Stock Exchange (NYSE). This hybrid model brings together a self organizing map (SOM) with a multilayer perceptron with back propagation algorithm (MLP-BP). The SOM aims to segment the database into different clusters, where the differences between them are highlighted. The MLP-BP is used to construct a descriptive mathematical model that describes the relationship between the indicators and the closing value of each cluster. The model was developed from a database consisting of the NYSE Composite US 100 Index over the period of 2 April 2004 to 31 December 2015. As input variables for neural networks, ten technical financial indicators were used. The model results were fairly accurate, with a mean absolute percentage error varying between 0.16% and 0.38%. View Full-Text
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Beluco, A.; Bandeira, D.L.; Beluco, A. Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model. J. Risk Financial Manag. 2017, 10, 6.
Beluco A, Bandeira DL, Beluco A. Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model. Journal of Risk and Financial Management. 2017; 10(1):6.Chicago/Turabian Style
Beluco, Adriano; Bandeira, Denise L.; Beluco, Alexandre. 2017. "Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model." J. Risk Financial Manag. 10, no. 1: 6.
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